Use auto profiler to set branch weights

Summary:
This adds a new scalar pass that reads a file with samples generated
 by 'perf' during runtime. The samples read from the profile are
 incorporated and emmited as IR metadata reflecting that profile.

 The profile file is assumed to have been generated by an external
 profile source. The profile information is converted into IR metadata,
 which is later used by the analysis routines to estimate block
 frequencies, edge weights and other related data.

 External profile information files have no fixed format, each profiler
 is free to define its own. This includes both the on-disk representation
 of the profile and the kind of profile information stored in the file.
 A common kind of profile is based on sampling (e.g., perf), which
 essentially counts how many times each line of the program has been
 executed during the run.

 The SampleProfileLoader pass is organized as a scalar transformation.
 On startup, it reads the file given in -sample-profile-file to
 determine what kind of profile it contains.  This file is assumed to
 contain profile information for the whole application. The profile
 data in the file is read and incorporated into the internal state of
 the corresponding profiler.

 To facilitate testing, I've organized the profilers to support two file
 formats: text and native. The native format is whatever on-disk
 representation the profiler wants to support, I think this will mostly
 be bitcode files, but it could be anything the profiler wants to
 support. To do this, every profiler must implement the
 ExternalProfile::loadNative() function.

 The text format is mostly meant for debugging. Records are separated by
 newlines, but each profiler is free to interpret records as it sees fit.
 Profilers must implement the ExternalProfile::loadText() function.

 Finally, the pass will call ExternalProfile::emitAnnotations() for each
 function in the current translation unit. This function needs to
 translate the loaded profile into IR metadata, which the analyzer will
 later be able to use.

 This patch implements the first steps towards the above design. I've
 implemented a sample-based flat profiler. The format of the profile is
 fairly simplistic. Each sampled function contains a list of relative
 line locations (from the start of the function) together with a count
 representing how many samples were collected at that line during
 execution. I generate this profile using perf and a separate converter
 tool.

 Currently, I have only implemented a text format for these profiles. I
 am interested in initial feedback to the whole approach before I send
 the other parts of the implementation for review.
 This patch implements:

 - The SampleProfileLoader pass.
 - The base ExternalProfile class with the core interface.
 - A SampleProfile sub-class using the above interface. The profiler
   generates metadata sampleprofile.samples on every IR instruction that
   matches the profiles.
 - A text loader class to assist the implementation of
   ExternalProfile::loadText().
 - Basic unit tests for the pass.

 Additionally, the patch uses profile information to compute branch
 weights based on instruction samples.

 This patch converts instruction samples into branch weights. It
 does a fairly simplistic conversion:

 Given a multi-way branch instruction, it calculates the weight of
 each branch based on the maximum sample count gathered from each
 target basic block.

 Note that this assignment of branch weights is somewhat lossy and can be
 misleading. If a basic block has more than one incoming branch, all the
 incoming branches will get the same weight. In reality, it may be that
 only one of them is the most heavily taken branch.

 I will adjust this assignment in subsequent patches.

Test Plan: accepted, implementing here first for full on testing

Reviewers: randall.rushing, synergy

Reviewed By: synergy

Differential Revision: http://review.codefirex.com/D28